Sample compression bounds for decision trees

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 799-806

  Shah, Mohak ^a  

^a LAVAL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA COMPRESSION; ERROR ANALYSIS; LEARNING ALGORITHMS; RISK MANAGEMENT;

OCCAM'S RAZOR BOUNDS; PRUNING STRATEGIES; RADEMACHER COMPLEXITIES; SAMPLE COMPRESSION; VC DIMENSION;

DECISION TREES;

EID: 34547972943     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273597     Document Type: Conference Paper

References (14)

1. Anthony, M., & Bartlett, P. (1999). Neural network learning: Theoretical foundations. Cambridge: Cambridge University Press.
2. Bartlett, P., & Mendelson, S. (2002). Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3, 463-482.
3. Blum, A., & Langford, J. (2003). PAC-MDL bounds. Proceedings of 16th Annual Conference on Learning Theory, COLT 2003, Washington, DC, August 2003 (pp. 344-357). Springer, Berlin.
4. Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. (1987). Occam's razor. Information Processing Letters, 24, 377-380.
5. Freund, Y. (1998). Self bounding learning algorithms. COLT: Proceedings of the Workshop on Computational Learning Theory (pp. 247-258). Morgan Kaufmann Publishers.
6. Golea, M., Bartlett, P., Lee, W. S., & Mason, L. (1998). Generalization in decision trees and DNF: Does size matter? Advances in Neural Information Processing Systems. The MIT Press.
7. Kearns, M., & Mansour, Y. (1998). A fast, bottom-up decision tree pruning algorithm with near-optimal generalization. Proc. 15th International Conf. on Machine Learning (pp. 269-277). Morgan Kaufmann, San Francisco, CA.
8. Langford, J. (2005). Tutorial on practical prediction theory for classification. Journal of Machine Learning Research, 3, 273-306.
9. Langford, J., & Blum, A. (1999). Microchoice bounds and self bounding learning algorithms. Computational Learing Theory (pp. 209-214).
10. Mansour, Y., & McAllester, D. (2000). Generalization bounds for decision trees. Proc. 13th Annu. Conference on Comput. Learning Theory (pp. 69-80). Morgan Kaufmann, San Francisco.
11. Shah, M. (2006). Sample compression, margins and generalization: Extensions to the set covering machine. Doctoral dissertation, SITE, University of Ottawa, Ottawa, Canada.
12. Shah, M., Sokolova, M., & Szpakowicz, S. (2006). Process-specific information for learning e-negotiation outcomes. Fundamenta Informaticae, 74, 351-373.
13. Vapnik, V. (1982). Estimation of dependences based on empirical data. New York: Springer-Verlag.
14. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques, 2nd ed. San Francisco: Morgan Kaufmann.